Adaptive Biometric Strategy using Doddington Zoo Classification of User's Keystroke Dynamics

Abir Mhenni (1, 2) , Estelle Cherrier (3) , Christophe Rosenberger (4) , Najoua Essoukri Ben Amara (1)

1 LATIS - Laboratory of Advanced Technology and Intelligent Systems
2 ENIT - Ecole Nationale d'Ingénieurs de Tunis
3 Equipe Automatique - Laboratoire GREYC - UMR6072
4 Equipe SAFE - Laboratoire GREYC - UMR6072

Securing personal, professional and even official data is a very critical issue nowadays, giving that these infor-mations are safeguarded in different devices (mobile, computer) and various accounts (social networks, e-mails). To protect them from unauthorized acess, users generally are asked to use passwords. But using only these authentication solutions is no longer efficient against hacker attacks. Keystroke dynamics is a biometric promising modality that guarantees the recognition of the user's characteristics; his typing manner on the keyboard. Regarding that the typing rythm of the user changes over time, adaptive biometric solutions help to take into consideration these variations. In this paper we classify user into multiple categories according to Doddonghton Zoo classification. Afterwards, we apply an adaptive strategy specific to each category of users. The achived experiments demonstrate that an update strategy specific to the user class has improved significantly the obtained performances.

Keywords: Users classifi- cation; Authentication; Password security; Keystroke dynamics; Adaptive strategy; Doddonghton Zoo

Domains: Computer Science [cs]; Cryptography and Security [cs.CR]

Abir Mhenni, Estelle Cherrier, Christophe Rosenberger, Najoua Essoukri Ben Amara. Adaptive Biometric Strategy using Doddington Zoo Classification of User's Keystroke Dynamics. 14th International Wireless Communications and Mobile Computing Conference (IWCMC), Jun 2018, Limassol, Cyprus. ⟨hal-01761086⟩